Flood susceptibility mapping using GIS-based analytic network process: A case study of Perlis, Malaysia

Dano, UL; Balogun, AL; Matori, AN; Yusouf, KW; Abubakar, IR; Mohamed, MAS; Aina, YA; Pradhan, B

Flood susceptibility mapping using GIS-based analytic network process: A case study of Perlis, Malaysia

Dano, UL Balogun, AL Matori, AN Yusouf, KW Abubakar, IR Mohamed, MAS Aina, YA Pradhan, B

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Publication Type:: Journal Article
Citation:: Water (Switzerland), 2019, 11 (3)
Issue Date:: 2019-03-01

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Field	Value	Language
dc.contributor.author	Dano, UL	en_US
dc.contributor.author	Balogun, AL	en_US
dc.contributor.author	Matori, AN	en_US
dc.contributor.author	Yusouf, KW	en_US
dc.contributor.author	Abubakar, IR	en_US
dc.contributor.author	Mohamed, MAS	en_US
dc.contributor.author	Aina, YA	en_US
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054	en_US
dc.date.issued	2019-03-01	en_US
dc.identifier.citation	Water (Switzerland), 2019, 11 (3)	en_US
dc.identifier.uri	http://hdl.handle.net/10453/135340
dc.description.abstract	© 2019 by the authors. Understanding factors associated with flood incidence could facilitate flood disaster control and management. This paper assesses flood susceptibility of Perlis, Malaysia for reducing and managing their impacts on people and the environment. The study used an integrated approach that combines geographic information system (GIS), analytic network process (ANP), and remote sensing (RS) derived variables for flood susceptibility assessment and mapping. Based on experts' opinion solicited via ANP survey questionnaire, the ANP mathematical model was used to calculate the relative weights of the various flood influencing factors. The ArcGIS spatial analyst tools were used in generating flood susceptible zones. The study found zones that are very highly susceptible to flood (VHSF) and those highly susceptible to flood (HSF) covering 38.4% (30,924.6 ha) and 19.0% (15,341.1 ha) of the study area, respectively. The results were subjected to one-at-a-time (OAT) sensitivity analysis to verify their stability, where 6out of the 22 flood scenarios correlated with the simulated spatial assessment of flood susceptibility. The findings were further validated using real-life flood incidences in the study area obtained from satellite images, which confirmed that most of the flooded areas were distributed over the VHSF and HSF zones. This integrated approach enables network model structuring, and reflects the interdependences among real-life flood influencing factors. This accurate identification of flood prone areas could serve as an early warning mechanism. The approach can be replicated in cities facing flood incidences in identifying areas susceptible to flooding for more effective flood disaster control.	en_US
dc.relation.ispartof	Water (Switzerland)	en_US
dc.relation.isbasedon	10.3390/w11030615	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Flood susceptibility mapping using GIS-based analytic network process: A case study of Perlis, Malaysia	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	11	en_US
utslib.for	0406 Physical Geography and Environmental Geoscience	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	open_access	*
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	11	en_US

Abstract:

© 2019 by the authors. Understanding factors associated with flood incidence could facilitate flood disaster control and management. This paper assesses flood susceptibility of Perlis, Malaysia for reducing and managing their impacts on people and the environment. The study used an integrated approach that combines geographic information system (GIS), analytic network process (ANP), and remote sensing (RS) derived variables for flood susceptibility assessment and mapping. Based on experts' opinion solicited via ANP survey questionnaire, the ANP mathematical model was used to calculate the relative weights of the various flood influencing factors. The ArcGIS spatial analyst tools were used in generating flood susceptible zones. The study found zones that are very highly susceptible to flood (VHSF) and those highly susceptible to flood (HSF) covering 38.4% (30,924.6 ha) and 19.0% (15,341.1 ha) of the study area, respectively. The results were subjected to one-at-a-time (OAT) sensitivity analysis to verify their stability, where 6out of the 22 flood scenarios correlated with the simulated spatial assessment of flood susceptibility. The findings were further validated using real-life flood incidences in the study area obtained from satellite images, which confirmed that most of the flooded areas were distributed over the VHSF and HSF zones. This integrated approach enables network model structuring, and reflects the interdependences among real-life flood influencing factors. This accurate identification of flood prone areas could serve as an early warning mechanism. The approach can be replicated in cities facing flood incidences in identifying areas susceptible to flooding for more effective flood disaster control.

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http://hdl.handle.net/10453/135340